Faurya is a privacy-focused web analytics platform that helps you understand your website visitors, track marketing performance, and grow your revenue without compromising user privacy.

Is Faurya GDPR compliant?

Faurya is designed for privacy-aware analytics workflows. Customers should review the Privacy Policy and Data Processing Agreement and apply consent and notice controls required for their jurisdiction.

How is Faurya different from Google Analytics?

Faurya focuses on practical growth reporting with source, funnel, journey, and outcome context in one workflow. Teams should compare tools based on implementation needs, reporting depth, and policy requirements.

Analytics MCP tools for traffic, visitors, sessions, events, revenue, attribution, cohorts, paths, and AI crawler data.

Analytics tools

Name: Faurya
Availability: InStock
Rating: 4.9 (120 reviews)
Author: Faurya

The analytics feature is read-only. It allows an approved MCP client to inspect Faurya analytics for permitted sites without changing configuration.

Supported access levels

Access	Supported	Allows
Read	Yes	Analytics queries, catalogs, visitors, sessions, events, payments, paths, attribution, cohorts, realtime snapshots, AI crawler analytics, and site context.
Write	No	Analytics does not expose write tools.

Role requirements and site scoping

Analytics tools are available to connections with analytics:read for the selected site. Supply siteId when the connection has multiple permitted sites. Hidden events are excluded and public metrics, dimensions, filters, operators, event names, and goal names should be discovered with faurya_get_analytics_catalog.

Analytics query model

faurya_query_analytics, faurya_analyze_goals, faurya_analyze_revenue, faurya_analyze_cohorts, and faurya_analyze_attribution share the semantic analytics model:

Parameter	Details
`mode`	`summary`, `timeseries`, `breakdown`, `distribution`, `top_values`, `compare_periods`, or `compare_segments`; default `summary`.
`period`	Optional object with `start`, `end`, and `timezone` defaulting to `UTC`.
`filters`	Recursive public filter object using `and`, `or`, `not`, `field`, `operator`, `value`, and `values`.
`metrics`	Up to 20 public metric names. Use `faurya_get_analytics_catalog` for the current list.
`dimensions`	Up to 5 public dimensions, or tool-specific lower limits where documented.
`granularity`	`hour`, `day`, `week`, `month`, `quarter`, or `year`.
`limit`	Positive integer up to `5000` for analytics queries unless a tool specifies a smaller maximum.
`siteId`	Optional when one site is permitted; required for multi-site connections.

Tool summary

Tool	Access	Purpose
`faurya_get_site_context`	Read	Reads site context, coverage, goals, currency/timezone, capabilities, and warnings.
`faurya_get_analytics_catalog`	Read	Lists public metrics, dimensions, operators, visible events, goals, properties, granularities, and capabilities.
`faurya_list_dimension_values`	Read	Discovers common values for one public dimension.
`faurya_query_analytics`	Read	Runs semantic analytics without SQL.
`faurya_get_realtime_snapshot`	Read	Returns recent active visitor and event snapshot.
`faurya_search_visitors`	Read	Cursor-paginated visitor search.
`faurya_get_visitor`	Read	Reads one visitor's lifetime analytics summary.
`faurya_trace_visitor`	Read	Reads a chronological visitor trace grouped by session.
`faurya_search_sessions`	Read	Cursor-paginated session search.
`faurya_get_session`	Read	Reads one reconstructed visible session timeline.
`faurya_search_events`	Read	Cursor-paginated visible event search.
`faurya_find_journeys`	Read	Finds journeys matching ordered behavior steps.
`faurya_analyze_goals`	Read	Analyzes goal completions and conversion rates.
`faurya_analyze_paths`	Read	Analyzes ranked paths with bounded depth.
`faurya_analyze_cohorts`	Read	Analyzes cohorts and retention.
`faurya_search_payments`	Read	Cursor-paginated payment event search.
`faurya_analyze_revenue`	Read	Analyzes revenue and payment metrics.
`faurya_analyze_attribution`	Read	Analyzes controlled attribution models.
`faurya_query_ai_crawls`	Read	Queries AI crawler analytics.
`faurya_analyze_changes`	Read	Compares periods and highlights changes.

Common analytics errors

SITE_SCOPE_REQUIRED, INSUFFICIENT_SCOPE, VALIDATION_ERROR, INVALID_FILTER, INVALID_FIELD, INVALID_OPERATOR, INVALID_LIMIT, INVALID_DATE_RANGE, INVALID_CURSOR, QUERY_TOO_COMPLEX, QUERY_TIMEOUT, DATA_NOT_AVAILABLE, and RATE_LIMITED.

`faurya_get_site_context`

What it does

Returns authenticated site context, available date coverage, configured goals, currency/timezone, capabilities, and data-source warnings. No secrets or internal IDs are returned.

Permission required

Analytics: Read.

When to use it

Use before analysis to understand the site, time coverage, configured goals, currency, timezone, and any non-fatal data warnings.

Example prompt

Summarize the context and available date coverage for my Faurya site.

Input

Parameter	Type	Required	Description
`siteId`	`string`	Conditional	Required when the connection has multiple permitted sites.

Example MCP input

{ "siteId": "site_example" }

Output

data contains safe site context, date coverage, goals, currency/timezone, capabilities, and warnings.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "site": {
      "id": "site_example",
      "domain": "example.com",
      "timezone": "UTC",
      "currency": "USD"
    },
    "coverage": {
      "firstEventAt": "2026-07-01T00:00:00.000Z",
      "lastEventAt": "2026-07-18T00:00:00.000Z"
    },
    "goals": ["signup"],
    "capabilities": ["analytics", "revenue", "ai_crawls"],
    "warnings": []
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

SITE_SCOPE_REQUIRED, INSUFFICIENT_SCOPE, SITE_NOT_FOUND.

Notes

This is safe to call repeatedly and does not expose secrets.

`faurya_get_analytics_catalog`

What it does

Lists public metrics, dimensions, semantic fields, operators, visible event names, goal names, approved property keys, granularities, attribution models, and tool capabilities. Hidden event types are excluded.

Permission required

Analytics: Read.

When to use it

Use it before constructing filters, metrics, dimensions, attribution requests, or event searches.

Example prompt

Show the analytics metrics and dimensions I can query.

Input

Parameter	Type	Required	Description
`siteId`	`string`	Conditional	Required for multi-site connections.

Example MCP input

{ "siteId": "site_example" }

Output

data contains allowlisted metrics, dimensions, operators, granularities, attribution models, visible event and goal names, public property keys, and capabilities.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "metrics": ["visitors", "sessions", "pageviews", "revenue"],
    "dimensions": ["country", "browser", "source", "path"],
    "operators": ["equals", "contains", "in"],
    "granularities": ["day", "week", "month"],
    "attributionModels": ["first_touch", "last_touch", "linear"]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

SITE_SCOPE_REQUIRED, INSUFFICIENT_SCOPE.

Notes

Use this as the source of truth for public analytics vocabulary. The MCP tools do not accept SQL.

`faurya_list_dimension_values`

What it does

Discovers values for a public dimension and returns value/count rows with optional search and limit.

Permission required

Analytics: Read.

When to use it

Use before building filters for countries, browsers, paths, campaigns, sources, events, or goals.

Example prompt

List the top campaign values for the last month.

Input

Parameter	Type	Required	Description
`field`	enum	Yes	One of `country`, `region`, `city`, `browser`, `os`, `device`, `referrer`, `path`, `hostname`, `source`, `medium`, `campaign`, `term`, `content`, `referrer_host`, `event_name`, `goal_name`.
`period`	object	No	`start`, `end`, `timezone`.
`search`	string	No	Search text, max 256 chars.
`limit`	integer	No	Positive integer, max `5000`.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "field": "campaign",
  "period": { "start": "2026-07-01", "end": "2026-07-18", "timezone": "UTC" },
  "limit": 10
}

Output

data contains dimension value rows with counts.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "field": "campaign",
    "rows": [{ "value": "summer_launch", "count": 42 }]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

VALIDATION_ERROR, INVALID_DATE_RANGE, INSUFFICIENT_SCOPE.

Notes

Values are based on visible public analytics fields.

`faurya_query_analytics`

What it does

Runs semantic analytics in summary, timeseries, breakdown, distribution, top-values, compare-periods, or compare-segments mode. SQL is not accepted.

Permission required

Analytics: Read.

When to use it

Use for aggregate traffic, conversion, revenue, metric, dimension, and period-comparison questions.

Example prompt

Compare visitors and conversions this week with the previous week.

Input

Parameter	Type	Required	Description
`mode`	enum	No	`summary`, `timeseries`, `breakdown`, `distribution`, `top_values`, `compare_periods`, `compare_segments`; default `summary`.
`period`	object	No	`start`, `end`, `timezone`.
`filters`	object	No	Public semantic filter tree.
`metrics`	string[]	No	1-20 public metrics.
`dimensions`	string[]	No	Up to 5 public dimensions.
`granularity`	enum	No	`hour`, `day`, `week`, `month`, `quarter`, `year`.
`limit`	integer	No	Max `5000`.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "mode": "timeseries",
  "period": { "start": "2026-07-11", "end": "2026-07-18", "timezone": "UTC" },
  "metrics": ["visitors", "conversions"],
  "granularity": "day"
}

Output

data contains the requested semantic analytics result, such as totals, rows, timeseries buckets, or comparison objects.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "mode": "timeseries",
    "period": { "start": "2026-07-11", "end": "2026-07-18", "timezone": "UTC" },
    "metrics": ["visitors", "conversions"],
    "rows": [{ "date": "2026-07-11", "visitors": 120, "conversions": 8 }]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_FILTER, INVALID_FIELD, INVALID_OPERATOR, INVALID_LIMIT, QUERY_TOO_COMPLEX, QUERY_TIMEOUT.

Notes

Call faurya_get_analytics_catalog first for current metric and dimension names.

`faurya_get_realtime_snapshot`

What it does

Returns current recent active visitor and event activity for the authenticated site using the last few minutes of visible events.

Permission required

Analytics: Read.

When to use it

Use for “what is happening now?” questions.

Example prompt

Show me current realtime activity for my site.

Input

Parameter	Type	Required	Description
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{ "siteId": "site_example" }

Output

data contains active visitor counts and recent visible events.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "activeVisitors": 7,
    "recentEvents": [
      {
        "type": "pageview",
        "path": "/pricing",
        "occurredAt": "2026-07-18T10:00:00.000Z"
      }
    ]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INSUFFICIENT_SCOPE, SITE_SCOPE_REQUIRED.

Notes

This is read-only and filters hidden events.

`faurya_search_visitors`

What it does

Searches visitors with acquisition, device, geography, event, page, goal, revenue, and semantic filter support.

Permission required

Analytics: Read.

When to use it

Use to find visitors matching behavior or attribution criteria.

Example prompt

Find visitors from organic search who completed a signup goal last week.

Input

Parameter	Type	Required	Description
`period`	object	No	`start`, `end`, `timezone`.
`filters`	object	No	Semantic filters.
`limit`	integer	No	Max `1000`, default bounded server-side.
`cursor`	string	No	Cursor from previous response.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "period": { "start": "2026-07-01", "end": "2026-07-18" },
  "limit": 25
}

Output

data contains visitor rows and may include nextCursor.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "visitors": [
      {
        "visitorId": "visitor_example",
        "firstSeenAt": "2026-07-12T09:00:00.000Z",
        "sessions": 3,
        "revenue": 49
      }
    ],
    "nextCursor": "eyJ0cyI6IjIwMjYtMDctMTJUMDk6MDA6MDAuMDAwWiIsImlkIjoidmlzaXRvcl9leGFtcGxlIn0"
  },
  "meta": {
    "warnings": [],
    "pagination": {
      "nextCursor": "eyJ0cyI6IjIwMjYtMDctMTJUMDk6MDA6MDAuMDAwWiIsImlkIjoidmlzaXRvcl9leGFtcGxlIn0",
      "hasMore": true
    }
  }
}

Common errors

INVALID_CURSOR, INVALID_FILTER, INVALID_LIMIT, INSUFFICIENT_SCOPE.

Notes

Cursors are opaque base64url JSON cursors containing timestamp and ID fields. Do not modify them.

`faurya_get_visitor`

What it does

Returns one visitor lifetime analytics summary including sessions, pageviews, visible events, goals, revenue, payments, acquisition, technology, geography, profile metadata, and engaged time.

Permission required

Analytics: Read.

When to use it

Use after finding a visitor ID to inspect that visitor’s complete safe summary.

Example prompt

Show the lifetime summary for visitor_example.

Input

Parameter	Type	Required	Description
`visitorId`	string	Yes	1-256 chars.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{ "siteId": "site_example", "visitorId": "visitor_example" }

Output

data contains the visitor summary and related safe analytics fields.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "visitorId": "visitor_example",
    "sessions": 3,
    "pageviews": 14,
    "goals": ["signup"],
    "revenue": 49
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

NOT_FOUND, INSUFFICIENT_SCOPE, VALIDATION_ERROR.

Notes

Use placeholders in prompts and avoid exposing real visitor IDs outside approved workflows.

`faurya_trace_visitor`

What it does

Returns a chronological visitor trace grouped by session with visible pageviews, custom events, goals, payments, milestones, and derived session data.

Permission required

Analytics: Read.

When to use it

Use to reconstruct a visitor journey across sessions.

Example prompt

Trace visitor_example and summarize their journey.

Input

Parameter	Type	Required	Description
`visitorId`	string	Yes	1-256 chars.
`limit`	integer	No	Max `1000`.
`cursor`	string	No	Cursor for next trace page.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{ "siteId": "site_example", "visitorId": "visitor_example", "limit": 50 }

Output

data contains grouped sessions, visible timeline events, and optional nextCursor.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "visitorId": "visitor_example",
    "sessions": [
      {
        "sessionId": "session_example",
        "events": [{ "type": "pageview", "path": "/" }]
      }
    ],
    "nextCursor": null
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_CURSOR, NOT_FOUND, INSUFFICIENT_SCOPE.

Notes

Hidden events never appear.

`faurya_search_sessions`

What it does

Searches sessions by date, entry/exit pages, technology, geography, visible event count, goals, payments, revenue, and semantic filters.

Permission required

Analytics: Read.

When to use it

Use to find sessions matching page, goal, revenue, or acquisition behavior.

Example prompt

Find sessions that entered on /pricing and completed signup.

Input

Parameter	Type	Required	Description
`period`	object	No	`start`, `end`, `timezone`.
`filters`	object	No	Semantic filters.
`limit`	integer	No	Max `1000`.
`cursor`	string	No	Cursor from previous response.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "period": { "start": "2026-07-01", "end": "2026-07-18" },
  "limit": 25
}

Output

data contains session rows and may include nextCursor.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "sessions": [
      {
        "sessionId": "session_example",
        "entryPage": "/pricing",
        "goals": ["signup"]
      }
    ],
    "nextCursor": null
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_CURSOR, INVALID_FILTER, INVALID_LIMIT.

Notes

Cursor pagination is supported.

`faurya_get_session`

What it does

Returns one reconstructed visible session timeline, summary metrics, payments, revenue, and engaged time.

Permission required

Analytics: Read.

When to use it

Use after finding a session ID to understand exactly what happened during that session.

Example prompt

Explain what happened in session_example.

Input

Parameter	Type	Required	Description
`sessionId`	string	Yes	1-256 chars.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{ "siteId": "site_example", "sessionId": "session_example" }

Output

data contains session summary and visible timeline.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "sessionId": "session_example",
    "pageviews": 5,
    "events": [{ "type": "pageview", "path": "/pricing" }],
    "revenue": 0
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

NOT_FOUND, INSUFFICIENT_SCOPE, VALIDATION_ERROR.

Notes

Only visible events are included.

`faurya_search_events`

What it does

Provides cursor-paginated access to literal visible events with semantic filters across event, visitor, session, page, referrer, geography, technology, goal/payment fields, and approved properties.

Permission required

Analytics: Read.

When to use it

Use when you need event-level evidence behind an aggregate result.

Example prompt

Show recent visible signup events with their page paths.

Input

Parameter	Type	Required	Description
`period`	object	No	`start`, `end`, `timezone`.
`filters`	object	No	Semantic filters.
`limit`	integer	No	Max `1000`.
`cursor`	string	No	Cursor from previous response.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "limit": 20,
  "filters": { "field": "event_name", "operator": "equals", "value": "signup" }
}

Output

data contains visible event rows and may include nextCursor.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "events": [
      {
        "eventId": "event_example",
        "eventName": "signup",
        "path": "/pricing",
        "occurredAt": "2026-07-18T10:00:00.000Z"
      }
    ],
    "nextCursor": null
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_CURSOR, INVALID_FILTER, INVALID_FIELD.

Notes

Hidden events are excluded.

`faurya_find_journeys`

What it does

Finds visitor or session journeys matching ordered behavioral step conditions and returns aggregate match count and bounded examples.

Permission required

Analytics: Read.

When to use it

Use to find examples of journeys that pass through a sequence of pages, events, or goals.

Example prompt

Find sessions that visited /pricing and then completed signup.

Input

Parameter	Type	Required	Description
`period`	object	No	`start`, `end`, `timezone`.
`steps`	array	Yes	1-10 steps. Each step has optional `type` (`page`, `event`, `goal`) and `value` max 512 chars.
`scope`	enum	No	`visitor` or `session`; default `session`.
`match`	enum	No	`strict` or `loose`; default `loose`.
`limit`	integer	No	Max `200`.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "steps": [
    { "type": "page", "value": "/pricing" },
    { "type": "goal", "value": "signup" }
  ],
  "scope": "session",
  "match": "loose"
}

Output

data contains counts and bounded example journeys.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "matchCount": 12,
    "examples": [{ "sessionId": "session_example", "matchedSteps": 2 }]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_JOURNEY_STEPS, INVALID_JOURNEY_STEP, INVALID_JOURNEY_SCOPE, INVALID_LIMIT.

Notes

Hidden events are excluded and example output is bounded.

`faurya_analyze_goals`

What it does

Analyzes goal completions, unique converters, conversion rates, time series, breakdowns, and revenue per converter where data supports it.

Permission required

Analytics: Read.

When to use it

Use for goal performance, conversion rate, and revenue-per-converter questions.

Example prompt

Analyze signup goal performance by source this month.

Input

Same semantic analytics fields as faurya_query_analytics: mode, period, filters, metrics, dimensions, granularity, limit, and conditional siteId.

Example MCP input

{
  "siteId": "site_example",
  "mode": "breakdown",
  "period": { "start": "2026-07-01", "end": "2026-07-18" },
  "dimensions": ["source"]
}

Output

data contains goal analytics rows, rates, or time buckets.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "mode": "breakdown",
    "rows": [
      { "source": "organic", "goalCompletions": 18, "conversionRate": 0.12 }
    ]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_FIELD, INVALID_FILTER, QUERY_TIMEOUT.

Notes

This is goal analytics, not goal setup. Use Tracking Setup tools for tracking code.

`faurya_analyze_paths`

What it does

Analyzes top visible paths starting from or ending at selected pages, goals, or payments with bounded depth and ranking.

Permission required

Analytics: Read.

When to use it

Use to understand navigation routes before or after a page or goal.

Example prompt

What paths lead users from the homepage to signup?

Input

Parameter	Type	Required	Description
`period`	object	No	`start`, `end`, `timezone`.
`startingPage`	string	No	Max 2048 chars.
`endingPage`	string	No	Max 2048 chars.
`endingGoal`	string	No	Max 512 chars.
`maximumDepth`	integer	No	Positive integer, max `5`.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "startingPage": "/",
  "endingGoal": "signup",
  "maximumDepth": 4
}

Output

data contains ranked path rows and counts.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": { "paths": [{ "path": ["/", "/pricing", "signup"], "count": 9 }] },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_PATH_DEPTH, INVALID_DATE_RANGE, INSUFFICIENT_SCOPE.

Notes

Path depth is bounded to control query cost.

`faurya_analyze_cohorts`

What it does

Analyzes first-seen, session, or acquisition cohorts and returns repeat visits, repeat goals/payments, and revenue retention using selected granularity.

Permission required

Analytics: Read.

When to use it

Use to inspect retention and revenue by acquisition or first-seen cohort.

Example prompt

Analyze weekly retention cohorts for visitors acquired in July.

Input

Same semantic analytics fields as faurya_query_analytics.

Example MCP input

{
  "siteId": "site_example",
  "mode": "timeseries",
  "period": { "start": "2026-07-01", "end": "2026-07-18" },
  "granularity": "week"
}

Output

data contains cohort rows or retention time buckets.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "cohorts": [
      {
        "cohort": "2026-W27",
        "visitors": 200,
        "returningVisitors": 48,
        "revenue": 320
      }
    ]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_FIELD, QUERY_TOO_COMPLEX, QUERY_TIMEOUT.

Notes

Use dimensions and granularity from the catalog.

`faurya_search_payments`

What it does

Searches payment events with transaction identifier, visitor/session, timestamp, amount, success/failure state, page, approved properties, and acquisition summary.

Permission required

Analytics: Read.

When to use it

Use to find payment events or investigate failed payments.

Example prompt

Show failed payment events from the last seven days.

Input

Parameter	Type	Required	Description
`period`	object	No	`start`, `end`, `timezone`.
`filters`	object	No	Semantic filters.
`limit`	integer	No	Max `1000`.
`cursor`	string	No	Cursor from previous response.
`failed`	boolean	No	Filter failed payment events.
`transactionId`	string	No	Max 256 chars.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{ "siteId": "site_example", "failed": true, "limit": 20 }

Output

data contains payment rows and optional nextCursor.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "payments": [
      {
        "transactionId": "txn_example",
        "amount": 49,
        "currency": "USD",
        "failed": true
      }
    ],
    "nextCursor": null
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

INVALID_CURSOR, INVALID_FILTER, INSUFFICIENT_SCOPE.

Notes

Provider secrets and private payment credentials are never returned.

`faurya_analyze_revenue`

What it does

Analyzes tracked revenue, successful/failed payments, paying visitors, average order value, revenue per visitor/converter, success rate, timeseries, and dimension breakdowns.

Permission required

Analytics: Read.

When to use it

Use for revenue dashboards, payment success-rate analysis, and revenue attribution follow-up.

Example prompt

Show revenue, paying visitors, and average order value by source this month.

Input

Same semantic analytics fields as faurya_query_analytics.

Example MCP input

{
  "siteId": "site_example",
  "mode": "breakdown",
  "metrics": ["revenue", "paying_visitors"],
  "dimensions": ["source"]
}

Output

data contains revenue metrics, rows, or time buckets.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "rows": [
      {
        "source": "organic",
        "revenue": 320,
        "payingVisitors": 8,
        "averageOrderValue": 40
      }
    ]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

DATA_NOT_AVAILABLE, INVALID_FIELD, QUERY_TIMEOUT.

Notes

Revenue results depend on tracked payment data.

`faurya_analyze_attribution`

What it does

Analyzes controlled attribution models and returns attributed visitors, conversions, payments, and revenue by selected dimensions.

Permission required

Analytics: Read.

When to use it

Use to compare first-touch, last-touch, linear, position-based, time-decay, and session-source attribution.

Example prompt

Compare last-touch revenue attribution by campaign this month.

Input

Parameter	Type	Required	Description
`model`	enum	No	`first_touch`, `last_touch`, `linear`, `position_based`, `time_decay`, `session_source`.
`dimension`	enum	No	`referrer`, `campaign`, `source`, `medium`, `landing_page`, `country`, `device`.
`mode`, `period`, `filters`, `metrics`, `dimensions`, `granularity`, `limit`, `siteId`	mixed	No/conditional	Same semantic analytics fields as `faurya_query_analytics`.

Example MCP input

{
  "siteId": "site_example",
  "model": "last_touch",
  "dimension": "campaign",
  "period": { "start": "2026-07-01", "end": "2026-07-18" }
}

Output

data contains attribution rows and selected model metadata.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "model": "last_touch",
    "dimension": "campaign",
    "rows": [{ "campaign": "summer_launch", "revenue": 220, "conversions": 6 }]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Common errors

VALIDATION_ERROR, INVALID_FIELD, QUERY_TIMEOUT.

Notes

The tool distinguishes controlled attribution fields from unrestricted SQL access.

`faurya_query_ai_crawls`

What it does

Queries AI crawler analytics by provider, agent, category, URL/path, status, source, timeseries/page rankings, coverage, frequency, and errors where data exists.

Permission required

Analytics: Read.

When to use it

Use to inspect AI crawler activity and content coverage.

Example prompt

Which AI crawlers visited my site most often this week?

Input

Parameter	Type	Required	Description
`period`	object	No	`start`, `end`, `timezone`.
`provider`	string	No	Max 128 chars.
`agent`	string	No	Max 256 chars.
`category`	string	No	Max 128 chars.
`path`	string	No	Max 2048 chars.
`source`	string	No	Max 128 chars.
`limit`	integer	No	Max `5000`.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "period": { "start": "2026-07-11", "end": "2026-07-18" },
  "limit": 10
}

Output

data contains AI crawler rows, coverage, frequency, or error summaries where available.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "rows": [
      {
        "provider": "ExampleAI",
        "agent": "ExampleBot",
        "path": "/docs",
        "visits": 12,
        "errors": 0
      }
    ]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

Investigate why conversions dropped compared with the previous week.

Input

Parameter	Type	Required	Description
`period`	object	No	Context period.
`baselinePeriod`	object	Yes	Baseline `start`, `end`, `timezone`.
`comparisonPeriod`	object	Yes	Comparison `start`, `end`, `timezone`.
`metrics`	string[]	No	1-10 public metrics.
`dimensions`	string[]	No	Up to 3 public dimensions.
`siteId`	string	Conditional	Required for multi-site connections.

Example MCP input

{
  "siteId": "site_example",
  "baselinePeriod": { "start": "2026-07-01", "end": "2026-07-07" },
  "comparisonPeriod": { "start": "2026-07-08", "end": "2026-07-14" },
  "metrics": ["visitors", "conversions"],
  "dimensions": ["source"]
}

Output

data contains metric deltas, percentage changes, contributing dimensions, and basic anomaly notes.

Example output

{
  "success": true,
  "requestId": "req_example",
  "data": {
    "changes": [
      {
        "metric": "conversions",
        "baseline": 40,
        "comparison": 30,
        "absoluteChange": -10,
        "percentChange": -25
      }
    ],
    "contributors": [{ "dimension": "source", "value": "paid", "change": -8 }]
  },
  "meta": {
    "warnings": [],
    "pagination": { "nextCursor": null, "hasMore": false }
  }
}

“Compare visitors and conversions this week with the previous week.”
“Find sessions that visited /pricing and then completed signup.”
“Which campaigns drove revenue this month?”
“Show failed payments in the last seven days.”
“What AI crawlers visited my docs pages?”